DOI: 10.3897/jucs.155783

Authors: Guohui Liu, Huan Zhang, Jianghong Li, Yanling Zhao, Xin Liu

Abstract: Long-term photovoltaic power prediction is crucial for the optimal dispatch of energy systems and the stability of power grids. However, existing methods are limited in accuracy when dealing with non-stationary signals such as intermittent fluctuations in light due to issues like spectral leakage and the rigidity of fixed convolutional kernel feature extraction. To address these challenges, this paper proposes a novel model, Wavelet-TimesNet, which integrates adaptive wavelet transform and multi-scale residual networks, aiming to enhance the robustness of long-term predictions. This model dynamically adjusts the parameters of the wavelet basis function to achieve multi-resolution analysis of local periodic features, effectively suppressing noise interference. It constructs a multi-scale residual network to capture local details such as hourly irradiance mutations and global trends such as seasonal power variations using parallel convolutional kernels of different sizes. An adaptive wavelet attention mechanism is introduced to dynamically weight and fuse frequency-domain and time-domain features, enhancing the focus on key information. Experiments were conducted based on photovoltaic datasets from Xinjiang's temperate continental climate and a subtropical monsoon climate region in China. The results show that in 96-hour predictions, Wavelet-TimesNet reduces the mean absolute error (MAE) by 4.97% and 3.29% in Xinjiang and China, respectively, and the mean squared error (MSE) by 7.80% and 5.75%. In 192-hour predictions, the mean squared percentage error (MSPE) of the Xinjiang dataset is reduced by 36.42%. Compared with advanced models such as Transformer and Informer, this model demonstrates significant advantages in handling non-stationary signals and capturing long-term trends, especially in extreme weather scenarios like sudden sandstorms and continuous rainy days, where prediction accuracy is notably improved. The research results provide an efficient solution for the precise dispatch of photovoltaic power stations, which is of great significance for reducing peak shaving costs in power grids, promoting the consumption of renewable energy, and facilitating the transformation of energy structures.

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DOI: 10.3897/jucs.156553

Authors: Ozan Can Acar, Hürevren Kılıç

Abstract: High-quality random number generation is a need for many engineering application areas grounded on different theoretical bases, including probability theory & statistics, game theory, information theory, etc. Our solution to the problem is a Pseudo-Random Number Generation (PRNG) framework constituted by a Two-Dimensional Linear Cellular Automata (2D LCA) model and the Monte Carlo Tree Search method within an agent-based gamified environment. The framework employs 2D LCA as the foundation, utilizing the temporal evolution histories of cells to examine its impact on random sequence generation enabled through an intelligent agent-based gamification approach. Experimental results and evaluations showed that under the proposed framework, both agent-to-agent and agent-to-environment goal-driven game competitions facilitate high-quality random sequence generation while passing the NIST Statistical Test Suite tests, with success rates surpassing most of the existing PRNGs in the literature.

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DOI: 10.3897/jucs.160204

Authors: Abdulhadi Altherwi, Md. Mottahir Alam, Mastoor M. Abushaega, Ahmed Hamzi, Abdulmajeed Azyabi, Shabbir Hassan, Asif Irshad Khan

Abstract: The integration of hybrid renewable energy systems (HRES) has introduced both opportunities and challenges in managing multisource power systems such as wind and solar. Accurate forecasting of HRES performance is critical to efficient planning and grid stability. This paper proposes a data efficient hybrid framework that combines Grey Wolf Optimization (GWO) for feature selection with Deep Belief Networks (DBN) for predictive modeling. GWO effectively selects relevant features from high dimensional environmental and system parameters, reducing computational burden and enhancing learning performance. The DBN is then trained on the optimized input set to forecast system performance. Two public datasets capturing wind and solar power production across distinct geographic conditions were used for validation. The proposed model significantly outperforms conventional methods, achieving a mean square error of 0.0207, RMSE of 0.144, and an energy efficiency of 98.32%. These results demonstrate the framework’s potential for deployment in smart grid forecasting environments.

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DOI: 10.3897/jucs.152399

Authors: Abdelhady Naguib, Abdulaziz Shehab

Abstract: The importance of localization algorithms is due to their uses in various wireless sensor network applications. A single anchor movement can be used to aid in localization to reduce the cost of using multiple anchors or equipping sensor nodes with GPS units, but the main challenge here is choosing the best path of movement while avoiding potential obstacles. This paper proposes a path planning algorithm called Square Spiral with Obstacle Avoidance (SQSPOA) which allows a mobile anchor node to track an optimal path while broadcasting its current coordinates to the unknown sensor nodes. During its movement, the mobile anchor node faces many obstacles that may hinder its mobility; but as a result of the superiority of the proposed algorithm the mobile anchor can avoid these obstacles while still broadcasting its coordinates to sensor nodes. The performance of the proposed algorithm was evaluated at the presence of variable-sized obstacles and was compared with recent path planning algorithms. Simulation results proved the superiority of the proposed algorithm with respect to localization error, percent of localized sensor node and trajectory length.

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DOI: 10.3897/jucs.148927

Authors: İbrahim Çakırlar, Sevcan Emek, Şebnem Bora, Oğuz Dikenelli

Abstract: This study introduces a novel methodology and framework for the verification, validation, and testing of agent-based simulation models: RatKit. Building on repeatable automated testing in ABMS, the present contribution significantly extends the foundation by proposing an integrated metamodel and systematic development methodology that embeds these activities throughout the simulation lifecycle. The RatKit methodology is both general, in that it applies to a wide range of agent-based simulation models using a well-defined metamodel, and comprehensive, in that it addresses the macro-level (societal), the meso-level (interaction) and the micro-level (agent) aspects of simulations. It also provides a generic infrastructure to be able to support various VV&T techniques. RatKit is designed as a general VV&T framework for all ABM frameworks. The methodology comes with a dedicated implemented framework. It is implemented by selecting the Repast ABM development framework. RatKit is demonstrated through a detailed case study of the Boids model, where the dynamics of alignment, cohesion, and separation are examined. Results from the case study show that a test-driven approach can enhance model reliability and ensure that individual agent behaviors coalesce into realistic emergent phenomena. Experiences and feedback obtained during the case studies show that developing ABM with a test-driven method based on VV&T facilitates the creation of desired models.

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DOI: 10.3897/jucs.143527

Authors: Nguyen Thi Hoang Phuong, Phan Minh Nhat, Nguyen Van Hieu

Abstract: Near-infrared (NIR) spectroscopy has emerged as a valuable analytical technique for assessing the composition and quality of various materials. This study proposes NirMACNet, a novel convolutional neural network (CNN) architecture that incorporates a residual-based multi-scale kernel mechanism for enhanced prediction of compositional attributes. The model is evaluated on two distinct NIR spectral datasets, milk and soil, to demonstrate its generalization capability across domains. By leveraging multiscale kernel operations, NirMACNet effectively captures diverse spectral patterns, while its deep architecture facilitates comprehensive feature extraction. To mitigate performance degradation commonly associated with deeper networks, residual learning is employed. Experimental results indicate that NirMACNet consistently outperforms state-of-the-art methods in terms of prediction accuracy. Future work will involve expanding the diversity of training datasets and investigating alternative architectural enhancements to further improve model robustness and applicability.

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DOI: 10.3897/jucs.140463

Authors: Daniel Spiekermann

Abstract: The evolution of modern data centers from traditional hardware-based infrastructures to highly virtualized environments has introduced new complexities in network traffic analysis. Virtual networks, characterized by dynamic changes in both overlay and underlay architectures, necessitate sophisticated methods for accurate anomaly detection and network analysis. This paper investigates the real-world behaviour of network traffic within virtualized environments, identifying the key factors that impact packet dynamics, including VM operations, multi-tenancy, user customization, and hardware adjustments. By defining the frequency and nature of these events, this research provides further details for the creation of accurate packet generation tools. These tools must simulate the dynamic characteristics of virtual networks, enabling reliable and realistic synthetic data generation. The research explains the need for enhanced packet generation methodologies to provide valid training and testing data for digital investigations, anomaly detection, and network simulations. As a result, this paper emphasizes the importance of developing accurate synthetic network traffic that mirrors real-world conditions and provides a valid basement for traffic analysis in virtual networks.

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DOI: 10.3897/jucs.129624

Authors: Thuy Phuong Khuat, Trang Van, Hoang Thien Van

Abstract: Deep learning methods are increasingly used in automated plant species classification systems to support biodiversity conservation and ecological monitoring, particularly for medicinal plants. This study presents a novel approach to plant leaf recognition by integrating the Vision Transformer (ViT) model with the OSSGabor filter, termed the OGViT method. The OSSGabor filter is a leaf feature extraction technique that combines the responses of Gabor filters in 16 directions and optimizes their parameters using the Structural Similarity Index Measure (SSIM). These features capture intricate details such as leaf veins, texture, and frequency variations, which are essential for enabling ViT to fully leverage deep learning for leaf recognition. Experimental results on four public datasets—Swedish Leaf, Flavia, Folio, and UCI Leaf—demonstrate that the OGViT method outperforms state-of-the-art approaches, achieving accuracy scores of 100%, 100%, 100%, and 98.88%, respectively, with a 20% testing set and an 80% training set. This performance highlights the effectiveness of the proposed method for plant classification, offering a robust tool with potential applications in agriculture and biodiversity conservation.

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DOI: 10.3897/jucs.121757

Authors: Phan Minh Nhat, Ngo Le Huy Hien, Dinh Minh Toan, Le Viet Hung, Phan Binh, Phung Thi Anh, Nguyen Thi Hoang Phuong, Nguyen Van Hieu

Abstract: The examination of Near Infrared Reflectance Spectroscopy (NIR) in cattle and poultry fertilizers provides a viable solution for determining optimal fertilizer composition for crop growth while mitigating adverse impacts on soil and groundwater quality. In recent studies, conventional machine learning models combined with spectral analysis have been used to ascertain cattle and poultry fertilizer concentrations. However, these traditional machine learning models encounter challenges in achieving data generalization, resulting in suboptimal prediction accuracy. To address this issue, this study proposes a synthesized machine learning model named EBAR (Error Based Accumulation Regression), which exhibits a commendable coefficient of determination, with an average R2 = 0.865 across 7 chemical substances, surpassing the performance of existing traditional machine learning models. Additionally, a Backward Elimination technique is designed to identify crucial wavelength ranges for monitoring component concentrations. The research outcome is promising and acts as a novel benchmark for later models in determining component concentrations through NIR spectroscopy. Future research gears toward expanding datasets and increasing samples of fertilizers, extending examined wavelength, and improving the model’s efficiency to apply to various types of foods, including seafood, vegetables, and fruits.

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DOI: 10.3897/jucs.106543

Authors: Melih Kuncan, Kaplan Kaplan, Yılmaz Kaya, Mehmet Recep Minaz, H. Metin Ertunç

Abstract: In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co-occurrence matrices. The culmination of this methodological framework is the identification of discernible co-occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10-fold cross-validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000 24000 26000 28000 and 30000 RPM.

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DOI: 10.3897/jucs.104901

Authors: Tien Quang Dam, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, Thang Le-Dinh

Abstract: In recent years, the rapid evolution of ransomware has led to the development of numerous techniques designed to evade traditional malware detection methods. To address this issue, a novel approach is proposed in this study, leveraging machine learning to encode critical information from Portable Executable (PE) headers into visual representations of ransomware samples. The proposed method selects highly impactful features for data sample classification and encodes them as images based on predefined color rules. A deep learning model named peIRCECon (PE Header-Image-based Ransomware Classification Ensemble with Concatenating) is also developed by integrating prominent architectures, such as VGG16 and ResNet50, and incorporating the concatenating method to enhance ransomware detection and classification performance. Experimental results using self-collected datasets demonstrate the efficacy of this approach, achieving high accuracy of 99.85% in distinguishing between ransomware and benign samples. This promising approach holds the potential to significantly improve the effectiveness of ransomware detection and classification, thereby contributing to more robust cybersecurity defense systems.

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DOI: 10.3897/jucs.102428

Authors: Ngo Le Huy Hien, Ah-Lian Kor, Mei Choo Ang, Eric Rondeau, Jean-Philippe Georges

Abstract: The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, and reduce detection time by up to 43.98% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study.

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DOI: 10.3897/jucs.112797

Authors: Lorenz Gillner, Ekaterina Auer

Abstract: In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.

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DOI: 10.3897/jucs.97170

Authors: Layse Santos Souza, Michel S. Soares

Abstract: A smart city is an urban centre that integrates a variety of solutions to improve infrastructure performance and achieve sustainable urban development. Urban roads are a crucial infrastructure highly demanded by citizens and organisations interested in their deployment, performance, and safety. Urban traffic signal control is an important and challenging real-world problem that aims to monitor and improve traffic congestion. The deployment of traffic signals for vehicles or pedestrians at an intersection is a complex activity that changes constantly, so it is necessary to establish rules to control the flow of vehicles and pedestrians. Thus, this article describes the joint use of the SmartCitySysML, a profile proposed by the authors, with TCPN (Timed Coloured Petri Nets) to refine and formally model SysML diagrams specifying the internal behaviour, and then verify the developed model to prove behavioural properties of an urban traffic signal control system.

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DOI: 10.3897/jucs.89703

Authors: Sergio-Daniel Sanchez-Solar, Gustavo Rodriguez-Gomez, Jose Martinez-Carranza

Abstract: Pendulum-Driven Spherical Robots are a type of spherical robot whose motion is achieved by controlling two motors for longitudinal and lateral motion. This configuration makes the robot a non-holonomic system, which impedes it from navigating directly towards a target. In addition, controlling its motion on inclined irregular surfaces is also an issue that has not received much attention. In this work, we addressed these two issues by proposing a methodology to control both motors using PID controllers. However, we propose tuning the controller’s gains using stochastic signals for the longitudinal controller because by varying the motor’s torque, the robot is more susceptible to destabilization in combination with a classical gain tuning methodology for the second controller. Our results indicate that this enables the robot to perform motion on inclined irregular surfaces. We also propose using semicircular trajectories to plan the robot’s motion to reach a target successfully even when moving on inclined irregular surfaces. We have carried out experiments in the Webots simulator, showing that our approach does not overshoot while reaching a settling time of almost 0. These results outperform the Ziegler-Nichols PID controller.

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DOI: 10.3897/jucs.94657

Authors: Nguyen Van Hieu, Ngo Le Huy Hien, Luu Van Huy, Nguyen Huy Tuong, Pham Thi Kim Thoa

Abstract: The natural ecosystem incorporates thousands of plant species and distinguishing them is normally manual, complicated, and time-consuming. Since the task requires a large amount of expertise, identifying forest plant species relies on the work of a team of botanical experts. The emergence of Machine Learning, especially Deep Learning, has opened up a new approach to plant classification. However, the application of plant classification based on deep learning models remains limited. This paper proposed a model, named PlantKViT, combining Vision Transformer architecture and the KNN algorithm to identify forest plants. The proposed model provides high efficiency and convenience for adding new plant species. The study was experimented with using Resnet-152, ConvNeXt networks, and the PlantKViT model to classify forest plants. The training and evaluation were implemented on the dataset of DanangForestPlant, containing 10,527 images and 489 species of forest plants. The accuracy of the proposed PlantKViT model reached 93%, significantly improved compared to the ConvNeXt model at 89% and the Resnet-152 model at only 76%. The authors also successfully developed a website and 2 applications called ‘plant id’ and ‘Danangplant’ on the iOS and Android platforms respectively. The PlantKViT model shows the potential in forest plant identification not only in the conducted dataset but also worldwide. Future work should gear toward extending the dataset and enhance the accuracy and performance of forest plant identification.

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DOI: 10.3897/jucs.97743

Authors: Xiaojuan Liao, Hui Zhang, Miyuki Koshimura, Rong Huang, Fagen Li

Abstract: Restricted preemption plays a crucial role in reducing total completion time while controlling preemption overhead. A typical version of restricted preemptive models is k-restricted preemptive scheduling, where preemption is only allowed after a task has been continuously processed for at least k units of time. Though solving this problem of minimizing the makespan on parallel machines is NP-hard in general, it is of vital importance to obtain the optimal solution for small-sized problems, as well as for evaluation of heuristics. This paper proposes optimal strategies to the aforementioned problem. Motivated by the dramatic speed-up of Boolean Satisfiability (SAT) solvers, we make the first step towards a study of applying a SAT solver to the k-restricted scheduling problem. We set out to encode the scheduling problem into propositional Boolean logic and determine the optimal makespan by repeatedly calling an off-the-shelf SAT solver. Moreover, we move one step further by encoding the problem into Partial Maximum Satisfiability (PMS), which is an optimized version of SAT, so that the explicit successive calls of the solver can be eliminated. The optimal makespan of the problem and the performance of the proposed methods are studied experimentally. Furthermore, an existing heuristic algorithm is evaluated by the optimization methods.

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DOI: 10.3897/jucs.89254

Authors: Fahimeh Ramazankhani, Mahdi Yazdian-Dehkord, Mehdi Rezaeian

Abstract: Features extracted from facial images are used in various fields such as kinship verification. The kinship verification system determines the kin or non-kin relation between a pair of facial images by analysing their facial features. In this research, different texture and color features have been used along with the metric learning method, to verify the kinship for the four kinship relations of father-son, father-daughter, mother-son and mother-daughter. First, by fusing effective features, NRML metric learning used to generate the discriminative feature vector, then SVM classifier used to verify to kinship relations. To measure the accuracy of the proposed method, KinFaceW-I and KinFaceW-II databases have been used. The results of the evaluations show that the feature fusion and NRML metric learning methods have been able to improve the performance of the kinship verification system. In addition to the proposed approach, the effect of feature extraction from the image blocks or the whole image is investigated and the results are presented. The results indicate that feature extraction in block form, can be effective in improving the final accuracy of kinship verification.

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DOI: 10.3897/jucs.75357

Authors: Jorge Ruiz Quiñones, Antonio J. Fernández Leiva

Abstract: Usually, human participation is required in order to provide feedback during the game tuning or balancing process. Moreover, this is commonly an iterative process in which play-testing is required as well as human interaction for gathering all important information to improve and tune the game components’ specification. In this paper, a mechanism is proposed to accelerate this process and reduce significantly the costs of it, contributing with a solution to perform the game parameter tuning and game balancing using search algorithms and artificial intelligence (AI) techniques. The process is executed in a fully automated way, and just requires a game specification written in a particular video game description language. Automated play-testing, and game’s feedback information analysis, are related to perform game parameters’ tuning and balancing, leading to offer a solution for the problem of optimizing a video game specification. Recently, XVGDL, a new language for specifying video games which is based on the eXtensible Markup Language (XML), has been presented. This paper uses XVGDL+, an extension of this lan- guage that incorporates new components to specify, within the video game specification, desirable goals or requirements to be evaluated after each game execution. A prototypical implementation of a Game Engine (termed XGE+) was also presented. This game engine not only enables the execution of an XVGDL+ game specification but also provides feedback information once the game has finished.The paper demonstrates that the combination of XVGDL+ with XGE+ offers a powerful mechanism for helping solving game AI research problems, in this case, the game tuning of video game parameters, with respect to initial optimization goals. These goals, as one of the particularities of the proposal presented here, are included within the game specification, minimizing the input of the process.As a practical proof of it, two experiments have been conducted to optimize a game specification written in XVGDL via a hill climbing local search method, in a fully automated way.

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DOI: 10.3897/jucs.94162

Authors: Zhiling Yang

Abstract: Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students' English learning behaviour, replacing teachers in part to answer students' questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.

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DOI: 10.3897/jucs.94160

Authors: Xin Ma

Abstract: Higher vocational education is a self-contained method of higher education that is aligned with global productivity and economic development. Its goal is to develop talented workers who contribute significantly to the economy and industry. Teaching analysis, teaching strategy, teaching practice, and assessment are all part of the course design process in high vocational education. Teaching assessment is one of the most effective methods for improving the quality of course teaching among teaching processes. This research proposes novel techniques in English teaching based on artificial intelligence for course selection based on students' feedback. Here, the dataset has been collected based on the students’ feedback on courses for Higher Vocational Education in English teaching. This dataset has been processed to remove invalid data, missing values, and noise. The processed data features have been dimensionality reduction integrated with K-means neural network. And the extracted features have been classified with higher accuracy using recursive elimination-based convolutional neural network. Based on this feedback data classification, recommendation for courses in Higher Vocational Education in English teaching has been suggested. The experimental analysis shows various students' feedback dataset validation and training in terms of accuracy of 96%, precision of 92%, recall of 93%, RMSE of 68%, and computational time of 65%.

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DOI: 10.3897/jucs.77793

Authors: Igor Tomicic, Markus Schatten, Vadym Shkarupylo

Abstract: This paper proposes an open ontology for self-sustainable human settlements in an effort to set the common language for modelling self-sustainable systems and address the issues regarding heterogeneity of physical devices, protocols, software components, data and message formats and other relevant factors, which proved to be unavoidable in implementations of smart systems in the domain of self-sustainability, smart homes, Internet of things, smart energy management systems, demand side systems, and related areas of research and engineering. Although the existing body of research is showing significant results in related, specialized research areas, currently there is no common formal language available which would bring the diversity of such research efforts under a single umbrella and thus enhance and integrate such efforts, which is often pointed out by the researchers in related fields. This paper discuses self- sustainable systems and associated areas, argues the need for the ontology development, presents its scope, development methodology, domain’s architecture and metamodel, and finally the proposed ontology itself, implemented in an open OWL format.

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DOI: 10.3897/jucs.73924

Authors: Nikola Zornić, Aleksandar Marković

Abstract: Traditionally, agent-based modelling and simulation relied on using utility function in agents’ decision-making process. Some drawbacks in this process are identified, and a potential remedy to the issue is proposed. This paper introduces a methodological framework for building a hybrid agent-based model that aims to overcome some of the elaborated problems related to the usage of a utility function. In the proposed approach, a machine learning algorithm substitutes the utility function, thus providing a possibility to use various algorithms. The proposed methodological framework has been applied to a case study of churn in a telecommunications company. Three models have been created and used for simulation experiments, two using the proposed methodology and one using utility function. The pros and cons of different approaches are identified and discussed.

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DOI: 10.3897/jucs.80750

Authors: Dagmara Łapczyńska, Konrad Łapczyński, Anna Burduk, Jose Machado

Abstract: The paper deals with a production scheduling process, which is a problematic and it requires considering a lot of various factors while making the decision. Due to the specificity of the production system analysed in the practical example, the production scheduling problem was classified as a Job-shop Scheduling Problem (JSP). The production scheduling process, especially in the case of JSP, involves the analysis of a variety of data simultaneously and is well known as NP-hard problem. The research was performed in partnership with a company from the automotive industry. The production scheduling process is a task that is usually performed by process engineers. Thus, it can often be affected by mistakes of human nature e.g. habits, differences in experience and knowledge of engineers (their know-how), etc. The usage of heuristic algorithms was proposed as the solution. The chosen methods are genetic and greedy algorithms, as both of them are suitable to resolve a problem that requires analysing a lot of data. The paper presents both approaches: practical and theoretical aspects of the usefulness and effectiveness of genetic and greedy algorithms in a production scheduling process.

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DOI: 10.3897/jucs.77103

Authors: Ekaterina Auer, Wolfram Luther

Abstract: In this paper, we consider genetic risk assessment and genetic counseling for breast cancer from the point of view of reliable uncertainty handling. In medical practice, there exist fairly accurate numerical tools predicting breast cancer (or gene mutation) probability based on such factors as the family history of a patient. However, they are too complex to be applied in normal doctors’ offices, so that several simplified, questionnaire-type support tools appeared. This process is highly affected by uncertainty. At the same time, reliability of test interpretations and counseling conclusions is especially important since they have direct influence on humans and their decisions. We show how expert opinions on mutation probabilities can be combined using the Dempster-Shafer theory. Based on multi-criteria binary decision trees and interval analysis, we combine the referral screening tool designed to determine patients at risk of breast cancer (and recommend genetic counseling or testing for them) with three further risk assessment tools available for this purpose. A patient’s confidence in the outcome of a genetic counseling session can be heightened by the proposed method since it combines different sources to provide score ranges leading to more information. Finally, based on this approach, a decision tree for assigning a risk category is proposed which enhances the existing methodology. The great impact of epistemic uncertainty is reflected through large overlapping intervals for the risk classes.

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DOI: 10.3897/jucs.68692

Authors: Riri Fitri Sari, Asri Ilmananda, Daniela Romano

Abstract: In the current digital era, information exchanges can be done easily through the Internet and social media. However, the actual truth of the news on social media platforms is hard to prove, and social media platforms are susceptible to the spreading of hoaxes. As a remedy, Blockchain technology can be used to ensure the reliability of shared information and can create a trusted communications environment. In this study, we propose a social media news spreading model by adapting an epidemic methodology and a scale-free network. A Blockchain-based news verification system is implemented to identify the credibility of the news and its sources. The effectiveness of the model is investigated by utilizing agent-based modelling using NetLogo software. In the simulations, fake news with a truth level of 20% are assigned a low News Credibility Indicator (NCI ± -0.637) value for all of the different network dimensions. Moreover, the Producer Reputation Credit is also decreased (PRC ± 0.213) so that the trust factor value is reduced. Our epidemic approach for news verification has also been implemented using Ethereum Smart Contract and several tools such as React with Solidity, IPFS, Web3.js, and Metamask. By showing the measurements of the credibility indicator and reputation credit to the user during the news dissemination process, this proposed smart contract can effectively limit user behaviour in spreading fake news and improve the content quality on social media.

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DOI: 10.3897/jucs.74166

Authors: Armando Cruz, Hugo Paredes, Leonel Morgado, Paulo Martins

Abstract: Virtual worlds, particularly those able to provide a three-dimensional physical space, have features that make them suitable to support collaborative activities. These features distinguish virtual worlds from other collaboration tools, but current taxonomies of the field of Computer-Supported Cooperative Work do not account for several distinctive features of virtual worlds, namely those related with non-verbal communication. We intended to find out how the use of an avatar, gestures, spatial sounds, etc., influence collaboration in order to be able to include non-verbal communication in taxonomies of the field Computer-Supported Cooperative Work. Several cases of collaboration in virtual worlds are analysed, to find the impact of these non-verbal characteristics of virtual worlds. We proposed adding the concept of Presence to taxonomies of Computer-Supported Cooperative Work and contribute with guidance for future taxonomy development that includes it as a new dimension. This new dimension of Presence is subdivided into "avatar" and "physical space" subdimensions. In turn, these are divided into "physical appearance", "gestures, sounds and animations" and "focus, nimbus and aura"; "environment" and "objects / artefacts". This new taxonomy-development proposal may contribute to inform better design of virtual worlds in support of cooperative work.

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DOI: 10.3897/jucs.70325

Authors: Manel Mena, Javier Criado, Luis Iribarne, Antonio Corral

Abstract: Cyber-Physical Systems (CPS) and Internet of Things (IoT) devices are handled by numerous different protocols. The management and connection to those devices tend to create usability and integrability issues. This brings about the need for a solution capable of facilitating the communication between different platforms and devices. The Web of Things (WoT) describes interfaces and interaction patterns among things, thereby abstracting itself from the underlying protocols used to manage those things and their implementation strategies. This paper describes the concept of Digital Dice, an abstraction of IoT devices and CPS capable of leveraging the advantages of microservices architectures and inspired by the concept of Digital Twins. A Digital Dice is a servient system of the WoT domain that represents a device by the features of the device, hence different WoT description models result in different microservices related to the particular thing. The paper explores the definition of Digital Dices and the conversion between WoT Thing Description Models and Digital Dices and the architecture that sustains the system.

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DOI: 10.3897/jucs.65164

Authors: David Bonilla Carranza, Adriana Peña Pérez Negrón, Madeleine Contreras

Abstract: Videogame development is software development; though, videogame projects require unusual developers’ technical and creative skills when compared with non-game projects. Based on the teaching experience of the Computer Simulation subject of the Computer Science Engineering program for undergraduate students, it was built a training strategy for videogame development projects oriented to software developers. As a result, it is presented the strategy described in terms of videogames for immersive virtual reality with open source platforms, but it can be adjusted to other technologies. This proposal links Project-Based Learning (PBL) with the SUM videogames development process, by including material, tools, and a creative perspective.

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DOI: 10.3897/jucs.2020.015

Authors: Yannick Pencolé, Audine Subias

Abstract: The overall context of this paper is the event-based behavior analysis and focuses on modeling and analyzing behaviors of interest involving time information. Any behavior of interest from any time event system is concisely defined as a set of time constrained events that must occur (positive behavior) and a set of time constrained events that must not occur (negative behavior). This article proposes a formal extension of the chronicle formalism that allows for the concise description of positive and negative behaviors. Based on this new formalism, several criteria are introduced, they formally characterize and compare a set of chronicles. A fully proved implementation of the proposed criteria is then described; it relies on the use of polyhedron techniques to solve systems of linear inequalities.

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DOI: 10.3897/jucs.2020.007

Authors: Anastasija Nikiforova, Janis Bicevskis, Zane Bicevska, Ivo Oditis

Abstract: The paper proposes a new data object-driven approach to data quality evaluation. It consists of three main components: (1) a data object, (2) data quality requirements, and (3) data quality evaluation process. As data quality is of relative nature, the data object and quality requirements are (a) use-case dependent and (b) defined by the user in accordance with his needs. All three components of the presented data quality model are described using graphical Domain Specific Languages (DSLs). In accordance with Model-Driven Architecture (MDA), the data quality model is built in two steps: (1) creating a platform-independent model (PIM), and (2) converting the created PIM into a platform-specific model (PSM). The PIM comprises informal specifications of data quality. The PSM describes the implementation of a data quality model, thus making it executable, enabling data object scanning and detecting data quality defects and anomalies. The proposed approach was applied to open data sets, analysing their quality. At least 3 advantages were highlighted: (1) a graphical data quality model allows the definition of data quality by non-IT and non-data quality experts as the presented diagrams are easy to read, create and modify, (2) the data quality model allows an analysis of "third-party" data without deeper knowledge on how the data were accrued and processed, (3) the quality of the data can be described at least at two levels of abstraction - informally using natural language or formally by including executable artefacts such as SQL statements.

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DOI: 10.3217/jucs-025-08-0967

Authors: Benjamin Weyers, Ekaterina Auer, Wolfram Luther

Abstract: We suggest to widen the focus of the scientific computations community from an isolated consideration of reliable numerical algorithms using standardized arithmetic to a broad user-centered system modeling and simulation approach relying on an appropriate verification and validation (V&V) design. Most V&V works rarely consider human-related issues specifically. However, modern applications generate and employ huge amounts of heterogeneous data and usually exhibit high complexity - challenges that are best tackled by augmenting human reasoning with automated techniques. That is, novel visual and collaborative approaches are needed to interpret the results, which has to be accounted for in the general V&V procedure. This should include an assessment of (meta-) data and code/outcome quality, selection of methods to propagate and bound uncertainty and, lastly, formally rigorous validation efforts. We present an approach to reliable visual analytics (i.e., analytics subjected to this V&V assessment), which can in turn contribute to the overall V&V procedure after that. Two use cases illustrate the potential of the introduced framework for reliable visual analytics.

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DOI: 10.3217/jucs-025-04-0390

Authors: Matias Micheletto, Rodrigo Santos, Javier Orozco

Abstract: In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.

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DOI: 10.3217/jucs-025-03-0203

Authors: Xiaoling Tao, Liyan Liu, Feng Zhao, Yan Huang, Saide Zhu

Abstract: With the rapid development of high-speed and large-scale complex network, network vulnerability data presents the characteristics of massive, multi-source and heterogeneous, which makes data fusion become more complex. Although existing data fusion methods can fuse multi-source data, they do not consider that the multisource data may affect the accuracy of fusion result. To solve this problem, we propose an ontology and weighted D-S evidence theory-based vulnerability data fusion method. In our method, we utilize ontology to describe the network vulnerability semantically and construct the network vulnerability ontology hierarchically. Then we use weighted D-S evidence theory to perform the operation of probability distribution and fusion processing. Besides, we simulate our method on MapReduce parallel computing platform. The experiment results show that our method is more effective and accurate compared with existing fusion approaches using single detection tool and traditional D-S evidence theory.

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DOI: 10.3217/jucs-024-12-1758

Authors: Darwin Alulema, Javier Criado, Luis Iribarne

Abstract: The Internet of Things has presented a rapid development, due to the over-crowding of hardware and software platforms, greater deployment of communications networks, development of data analysis tools, among others. This development has led to a boom in applications focused on areas as varied as Smart Cities, Smart Agro, Smart Buildings, Smart Home, and Smart Health, in which people and things are interconnected. This is one of the reasons by which a review of the main technologies involved in the emergence of the Internet of Things must be carried out to determine those characteristics allowing that interconnection, but without neglecting security. This issue allows the user to feel con dent to use these new services. In this work, we propose a cross-device architecture that integrates technologies and implementations in homes, and uses basic authentication as a security scheme. To validate the cross-device proposal, a case study scenario has been designed, including and integrating digital-TV (DTV), Smart Phones and wearables devices for monitoring users physical activity.

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DOI: 10.3217/jucs-024-10-1345

Authors: Fernando Ferri, Arianna D’Ulizia, Patrizia Grifoni

Abstract: This paper provides an analysis of the trends of the scientific production of language evolution models discussing the current developments and outlines the most promising future perspectives of this research field. A hybrid evaluation methodology has been applied in this study that integrates bibliometric and social research techniques to gain both quantitative and qualitative evidence of the research impact of language evolution models. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to reveal the developments in the field and to plan future research directions.

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DOI: 10.3217/jucs-023-11-1057

Authors: Giangiacomo Bravo, Mike Farjam

Abstract: Computational social sciences (CSS) refer to computer-enabled investigations of human behaviour and social interaction. They include three main components - (i) computational modelling and social simulation, (ii) the analysis of digital traces of online interactions, (iii) virtual labs and online experiments - and allow researchers to perform studies that were even hard to imagine a few decades ago. Moreover, CSS favour a more systematic test of theories and increase the possibility of study replication, two factors holding the potential to help social sciences reach a higher scientific status. Despite the huge potential of CSS, we follow previous works in identifying several impediments to a larger adoption of computational methods in social sciences. Most of them are linked with the humanistic attitude and a lack of technical skills of many social scientist. Significant changes in the basic training of social scientist and in the relation patterns with other disciplines and departments are needed before the potential of CSS can be fully exploited.

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DOI: 10.3217/jucs-023-11-1035

Authors: Giangiacomo Bravo, Mikko Laitinen, Magnus Levin, Welf Löwe, Göran Petersson

Abstract: The ubiquity of sensor, computing, communication, and storage technologies provides us with access to previously unknown amounts of data - Big Data. Big Data has revolutionized research communities and their scientific methodologies. It has, for instance, innovated the approaches to knowledge and theory building, validation, and exploitation taken in the engineering sciences. The humanities and social sciences even face a paradigm shift away from data-scarce, static, coarse-grained and simple studies towards data-rich, dynamic, high resolution, and complex observations and simulations. The present focused topic presents investigations from different research fields in which the focus is either on utilizing Big Data or charting the benefits of using such evidence in basic research.

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DOI: 10.3217/jucs-023-08-0725

Authors: Ulas Gulec, Murat Yilmaz, Veysi Isler

Abstract: Software development is a complex human endeavour with high failure rates. Although a variety of software development methodologies have been proposed to improve the software development process, there is no universal model for all software development organizations. Virtual reality (VR) is an emerging trend especially for the gaming industry, which should prepare itself for VR development. The goal of this study is to explore potential software development activities and determine whether designing a new software development methodology for VR projects is an important topic for software development organizations working on VR software development. For this purpose, a literature survey has been completed and 71 academic studies have been examined in detail. This study shows that no work is being conducted in the field of developing a new methodology for VR projects. However, the study does show that there are similar endeavours in the field of human computer interaction (HCI), such as game development methodology.

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DOI: 10.3217/jucs-023-05-0479

Authors: Ademir Constantino, Candido Ferreira Xavier De Mendonca Neto, Silvio De Araujo, Dario Landa-Silva, Rogério Calvi, Allainclair Flausino dos Santos

Abstract: The bus driver scheduling problem (BDSP) under study consists in finding a set of duties that covers the bus schedule from a Brazilian public transportation bus company with the objective of minimizing the total cost. A deterministic 2-phase heuristic algorithm is proposed using multiple assignment problems that arise from a model based on a weighted multipartite graph. In the first phase, the algorithm constructs an initial feasible solution by solving a number of assignment problems. In the second phase, the algorithm attempts to improve the solution by two different procedures. One procedure takes the whole set of duties and divides them in a set of partial duties which are recombined. The other procedure seeks to improve single long duties by eliminating the overtime time and inserting it into another duty. Computational tests are performed using large-scale real-world data with more than 2,300 tasks and random instances extracted from real data. Three different objective functions are analyzed. The overall results indicate that the proposed approach is competitive to solve large BDSP.

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DOI: 10.3217/jucs-022-06-0874

Authors: Piotr Jędrzejowicz, Aleksander Skakovski

Abstract: Differential evolution (DE) is a stochastic global optimization method, that has been under continuous development during the past two decades. It has been recognized that preserving the diversification of population can significantly improve the performance of DE. Although, several results and approaches to population diversification have been proposed, it seems that this issue still has a potential for development. In this paper we have studied experimentally the possibility of increasing the performance of DE. Our investigation aims at identifying how the performance of DE depends on such factors as population diversity, size and number of fitness function evaluations carried out by DE to yield a solution. In our experiments we diversified the population in an intensive manner using the proposed decloning procedure carried out in cycles, and also through increasing the population size. The choice of how to preserve the diversification may depend on restrictions imposed on the population size, response time, and the quality of solutions that should be met by a specific implementation of the algorithm. The obtained results allowed us to propose a performance improvement policy that might noteworthy improve both the efficacy and response time of the algorithm. The discrete-continuous scheduling with continuous resource discretisation was used as the test problem.

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DOI: 10.3217/jucs-022-04-0521

Authors: Muneer Yassein, Yaser Khamayseh, Mai Abujazoh

Abstract: The security issue is essential and more challenging in Mobile Ad-Hoc Network (MANET) due to its characteristics such as, node mobility, self-organizing capability and dynamic topology. MANET is vulnerable to different types of attacks. One of possible attacks is black hole attack. Black hole attack occurs when a malicious node joins the network with the aim of intercepting data packets which are exchanged across the network and dropping them which affects the performance of the network and its connectivity. This paper proposes a new dataset (BDD dataset) for black hole intrusion detection systems which contributes to detect the black hole nodes in MANET. The proposed dataset contains a set of essential features to build an efficient learning model where these features are selected carefully using one of the feature selection techniques which is information gain technique J48 decision tree, Naïve Bayes (NB) and Sequential Minimal Optimization (SMO) classifiers are learned using training data of BDD dataset and the performance of these classifiers is evaluated using a learning machine tool Weka 3.7.11. The obtained performance results indicate that using the proposed dataset features succeeded in build an efficient learning model to train the previous classifiers to detect the black hole attack.

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DOI: 10.3217/jucs-020-05-0640

Authors: Mauricio Alférez, Roberto Lopez-Herrejón, Ana Moreira, Vasco Amaral, Alexander Egyed

Abstract: Software Product Line Engineering (SPLE) is a successful paradigm to produce a family of products for a specific domain. A challenge in SPLE is to check that different models used in early SPL specification do not contain inconsistent information that may be propagated and generate inconsistent products that do not conform to its requirements. This challenge is difficult to address due to the high number of possible combinations of product features and model fragments specifying those features. Variability Consistency Checking (VCC) offers automatic means to address that challenge. VCC relates information inferred from the relationships between features and from base models related to those features. Validating if all the products in an SPL satisfy user-defined consistency constraints is based on searching for a satisfying assignment of each formula generated by VCC. We validated VCC and its supporting tool on two case studies from different application domains, the results were encouraging as we did not observed significant performance penalties.

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DOI: 10.3217/jucs-019-04-0484

Authors: Javier Torres-Niño, Alejandro Rodríguez-González, Ricardo Colomo-Palacios, Enrique Jiménez-Domingo, Giner Alor-Hernández

Abstract: Data mining is an important part of information management technology. Simply put, it is a method to extract and analyze meaningful patterns and correlations in a large relational database. In Data mining, Decision trees are one of the most worldwide used tools for decision support. In the emerging area of Data mining applications, users of data mining tools are faced with the problem of data sets that are comprised of large numbers of features and instances. Such kinds of data sets are not easy to handle for mining because decision trees generally depends on several parameters like dataset used and configuration of the tree itself among others in order to build an accurate model classification. In this work a novel hybrid classifier system is presented for improving accuracy of decision trees using clustering techniques. This system is formed by a clustering algorithm, a decision tree and an optional module for identifying appropriate parameters for the clustering algorithm. These three modules working together are capable to increase the accuracy of the solutions. The validation of the results of this work has been performed using several well-known datasets and applying two decision trees algorithms. The accuracy percentages are compared in order to show our proposal improvement, obtaining good results. Finally two clustering algorithms have been used to compare the accuracy between different proposals.

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DOI: 10.3217/jucs-019-01-0053

Authors: Hyun-Seok Min, Sang-Mun Chung, Jin-Young Choi

Abstract: Traditionally, System Analysis and Software Design are treated as separate processes. Software Design is based on System Analysis but they have little direct relationship to each other. UML (Unified Modeling Language) is widely accepted by industry as the de facto standard for System Analysis and Software Design. The primary tool for System Analysis is the Use Case Diagram and its Scenarios, while the primary tools for Software Design are the Class Diagram and Sequence Diagram. State Machine Diagram is also very useful for behavioral modeling. Our aim is to derive system behavior from software design, so that separate processes can work together. This paper suggests how to make a system-wide State Machine Diagram by gathering State Machine Diagrams in the system using predefined Stereotypes and Synchronization/Externalization. The resulting system-wide State Machine Diagram can be used for various things, such as automatic test case generation for the system, deadlock detection, and Use Case scenario consistency checking. The proposed method is applied to the Missile project of ADD (Agency for Defense Development) in South Korea. The result is very promising. It is expected to be applied to more projects.

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DOI: 10.3217/jucs-018-13-1888

Authors: Basabdatta Bhattacharya, Damien Coyle, Liam Maguire

Abstract: This paper describes a study of the effects of variation of synaptic connectivity in a thalamo-cortical circuitry using a neural mass model. The oscillatory behaviour of the model output is assessed within the alpha frequency band. The model presented here is a modification of an existing model involving the introduction of biologically plausible synaptic connectivities as well as synaptic structure. Our goal is to study altered event related desynchronisation/synchronisation (ERD/ERS) patterns within the alpha band in Alzheimers disease as observed in experimental studies. ERD is an amplitude attenuation of certain EEG rhythms when an event is initiated or while a certain event is taking place in the brain. ERS is an amplitude enhancement of a certain EEG rhythm when cortical areas are not specifically engaged in a given mode of activity at a certain instant of time. EEG desynchronisation normally blocks alpha rhythms in the EEG due to sensory processing or behaviour. The results show that a decrease in synaptic connectivity induces a time lag in both ERD and ERS peaks in the model output. Furthermore, a deficiency induced in the inhibitory cholinergic pathway results in a distinct effect on time to peak in the ERD/ERS response. These observations are consistent with experimental findings in AD. Variation of the level of interconnectivity has a pronounced effect on the ERS behaviour of the model while the excitatory connectivity in the retino-geniculate pathway during the resting state is more influential on the ERD behaviour.

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DOI: 10.3217/jucs-018-13-1871

Authors: Gurvinder Baicher

Abstract: This paper considers theoretical issues concerning reconstruction errors and conditions for perfect reconstruction (PR) of the input signal for a 2-channel multirate quadrature mirror filter (QMF) bank. The main emphasis is on the optimisation of a new design of a perfect reconstruction QMF bank using infinite impulse response (IIR) filters based on transformation of variables technique. The genetic algorithm (GA) optimisation is used for the initial design of the QMF bank and for the IIR filters using finite word length coefficients. The optimised results are then applied to a real time digital signal processing kit. Finally, some test results for data compression achievable using different values of encoded bits are included.

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DOI: 10.3217/jucs-018-13-1853

Authors: Musrrat Ali, Millie Pant, Atulya Nagar, Chang Ahn

Abstract: Insertion of a local search technique is often considered an effective mechanism to increase the efficiency of a global optimization algorithm. In this paper we propose and analyze the effect of two local searches namely; Trigonometric Local Search (TLS) and Interpolated Local Search (ILS) on the working of basic Differential Evolution (DE). The corresponding algorithms are named as DETLS and DEILS. The performances of proposed algorithms are investigated and compared with basic DE, modified versions of DE and some other evolutionary algorithms. It is found that the proposed schemes improve the performance of DE in terms of quality of solution without compromising with the convergence rate.

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DOI: 10.3217/jucs-018-10-1238

Authors: Robert Bucki, Petr Suchánek

Abstract: Rapidly changing business environment requires new approaches and methods for supporting management systems in all types of companies. Modern companies doing business use e-commerce systems by default. One of the key areas of e-commerce systems is logistics and the supply chain. The optimal way to ensure the success of logistics and supply chains is to use the methods of modeling and simulation based on appropriate models and especially its mathematical representation. In this paper, authors highlight the customer-oriented model of the e-commerce system and deal with logistic optimization and simulations. As an example, a sample logistic structure which requires the adequate control approach is presented. This is realized by means of heuristic algorithms which are responsible for meeting the set criterion. Moreover, the criteria to either maximize the production output or minimize the lost flow capacity of the logistic system or minimize the tool replacement criterion are introduced. Equations of state are given in order to represent the flow of material through the logistic system.

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DOI: 10.3217/jucs-018-09-1177

Authors: Xingang Liu, Jiantan Liu, Kook-Yeol Yoo, Haengrae Cho

Abstract: For the Wireless Multimedia Sensor Network (WMSN), the intraframe video coding is widely used for the robust transmission and computation complexity. Though the intraframe algorithm requires much smaller computational complexity than the interframe coding, the amount of computation of the intraframe should be reduced to use WMSN application. In this paper, we propose an intra mode decision algorithm to reduce the computation complexity of intraframe H.264/AVC encoders. The proposed algorithm determines the candidate modes and skips the remaining modes based on the smoothness and directional similarity of MB. The simulation results show that the proposed algorithm achieves 18% to 70% reduction in the computational complexity, compared with the various conventional methods.

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DOI: 10.3217/jucs-018-07-0917

Authors: Luneque Junior, Nadia Nedjah, Luiza Mourelle

Abstract: Network-on-Chip (NoC) have been used as an interesting option in design of communication infrastructures for embedded systems, providing a scalable structure and balancing the communication between cores. Because several data packets can be transmitted simultaneously through the network, an efficient routing strategy must be used in order to avoid congestion delays. In this paper, ant colony algorithms were used to find and optimize routes in a mesh-based NoC. The routing optimization is driven by the minimization of total latency in packets transmission. The simulation results show the effectiveness of the ant colony inspired routing by comparing it with general purpose algorithms for deadlock free routing under different traffic patterns.

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DOI: 10.3217/jucs-017-13-1863

Authors: Kun Ma, Bo Yang, Ajith Abraham

Abstract: With the help of model transformation, it is possible to generate target models from source models. A possible way to face iterative development process with frequent modifications is to use not only a single transformation but also frequent model synchronization. In this paper, we propose a relational approach to model transformation using Query/View/Transformations (QVT) Relations language that also provides model synchronization mechanism based on the version of the models. The proposed framework uses a Platform-Independent Business Model (PIM-BM) and a Platform-Specific Business Component Model (PSM-BC) via the extension of the UML metamodel and MOF at different levels of abstraction, which sufficiently describe both the structural and behavioral properties of generic Web applications. Also we present the typical model mapping rules between PIM-BMs and PSM-BCs using QVT Relations. Finally the model synchronization based on the version of models is provided for the above model transformation approach.

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DOI: 10.3217/jucs-017-12-1659

Authors: Rory Sie, Marlies Bitter-Rijpkema, Peter Sloep

Abstract: Several studies have shown that connecting to people in other networks foster creativity and innovation. However, it is often difficult to tell what the prospective value of such alliances is. Cooperative game theory offers an a priori estimation of the value of future collaborations. We present an agent-based social simulation approach to recommending valuable peers in networked innovation. Results indicate that power as such does not lead to a winning coalition in networked innovation. The recommendation proved to be successful for low-strength agents, which connected to high-strength agents in their network. Future work includes tests in real-life and other recommendation strategies.

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DOI: 10.3217/jucs-017-03-0349

Authors: Sandra Baldassarri, Francisco Seron

Abstract: This paper presents a system that simulates, in real time, the volumetric deformation of muscles during human locomotion. We propose a two-layered motion model. The requirements of realism and real time computation lead to a hybrid locomotion system that uses a skeleton as first layer. The muscles, represented by an anatomical surface model, constitute the second layer, whose deformations are simulated with a finite element method (FEM). The FEM subsystem is fed by the torques and forces got from the locomotion system, through a line of action model, and takes into account the geometry and material properties of the muscles. High level parameters (like height, weight, physical constitution, step frequency, step length or speed) allow to customize the individuals and the locomotion and therefore, the deformation of the persons' muscles.

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DOI: 10.3217/jucs-017-02-0243

Authors: Rafael Rieder, Márcio Pinho, Alberto Raposo

Abstract: This work describes a methodology that supports the design and implementation of software modules, which represent the individual and collaborative three-dimensional interaction process phases. The presented methodology integrates three modeling approaches: Petri Nets, a collaborative manipulation model based on the combination of single user interaction techniques taxonomy, and object-oriented programming concepts. The combination of these elements allows for the description of interaction tasks, the sequence of interaction processes being controlled by Petri Nets with the codes generated automatically. By the integration of these approaches, the present work addresses not only the entire development cycle of both individual and collaborative three-dimensional interaction, but also the reuse of developed interaction blocks in new virtual environment projects.

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DOI: 10.3217/jucs-016-20-3102

Authors: Bernhard Thalheim

Abstract: Conceptual modelling is a widely applied practice and has led to a large body of knowledge on constructs that might be used for modelling and on methods that might be useful for modelling. It is commonly accepted that database application development is based on conceptual modelling. It is however surprising that only very few publications have been published on a theory of conceptual modelling. Modelling is typically supported by languages that are well-founded and easy to apply for the description of the application domain, the requirements and the system solution. It is thus based on a theory of modelling constructs. At the same time, modelling incorporates a description of the application domain and a prescription of requirements for supporting systems. It is thus based on methods of application domain gathering. Modelling is also an engineering activity with engineering steps and engineering results. It is thus engineering. The first facet of modelling has led to a huge body of knowledge. The second facet is considered from time to time in the scientific literature. The third facet is underexposed in the scientific literature. This paper aims in developing principles of conceptual modelling. They cover modelling constructs as well as modelling activities as well as modelling properties. We first clarify the notion of conceptual modelling. Principles of modelling may be applied and accepted or not by the modeler. Based on these principles we can derive a theory of conceptual modelling that combines foundations of modelling constructs, application capture and engineering. A general theory of conceptual modelling is far too comprehensive and far too complex. It is not yet visible how such a theory can be developed. This paper therefore aims in introducing a framework and an approach to a general theory of conceptual modelling. We are however in urgent need of such a theory. We are sure that this theory can be developed and use this paper for the introduction of the main ingredients of this theory.

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DOI: 10.3217/jucs-016-20-2904

Authors: Alessander Benevides, Giancarlo Guizzardi, Bernardo Ferreira Bastos Braga, Joao Paulo Andrade Almeida

Abstract: Assessing the quality of conceptual models is key to ensure that conceptual models can be used effectively as a basis for understanding, agreement and construction of information systems. This paper proposes an approach to assess conceptual models defined in OntoUML by transforming these models into specifications in the logic-based language Alloy. These Alloy specifications include the modal axioms of the theory underlying OntoUML, allowing us to validate the modal meta-properties representing ontological commitments of the OntoUML types and relations.

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DOI: 10.3217/jucs-016-19-2780

Authors: Conor Gaffney, Declan Dagger, Vincent Wade

Abstract: This paper examines the personalization of online training simulations which are a key modern approach in computer aided education. More specifically it focuses on the difficulties involved in authoring personalized training simulations. The composition of such systems is very difficult which has hampered their wide spread adoption [Joolingen, 03]. Presently adaptive training simulations can only be authored by programmers working closely with subject matter experts. One of the key ways for adaptive simulations to increase their popularity in online eLearning [Wade, 09] is to reduce the effort and technical skills required by authors in their development. We argue that personalized online simulations need to be composed by subject matter experts, inexpensively and quickly. This paper details the twin challenges in composing content for both educational simulations and personalization. It also describes ACTSim, a new and unique composition tool that supports the rapid development of personalized training simulations. In particular ACTSim focuses on situational simulations for inter personal dialogue, so called soft skills. This paper concludes with a series of evaluations of the composition tool and of courses developed using the composition tool.

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DOI: 10.3217/jucs-016-17-2353

Authors: Giovanni Giachetti, Manuela Albert, Beatriz Marín, Oscar Pastor

Abstract: In a model-driven development context, the definition (or selection) of an appropriate modeling language is a crucial task. OMG, in the model-driven architecture specification, recommends the use of UML for model-driven developments. However, the lack of semantic precision in UML has led to different model-driven approaches proposing their own domain-specific modeling languages in order to introduce their modeling needs. This paper focuses on customizing the UML association in order to facilitate its application in model-driven development environments. To do this, a well-defined process is defined to integrate the abstract syntax of a domain-specific modeling language that supports a precise semantics for the association construct in UML by means of the automatic generation of a UML profile. Finally, a brief example shows how the results obtained by the application of the proposed process can generate software products through a real model compilation tool.

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DOI: 10.3217/jucs-016-15-1992

Authors: Kris Mcglinn, Eleanor Neill, Alan Gibney, Declan Sullivan, Dave Lewis

Abstract: The promise of smart buildings (SBs) is a safer more productive environment for users and a more operationally efficient building for owners. The automation of building function is highly dependent on sensing devices and Smart Building Applications(SBAs), which are often only evaluated in situ post deployment, making re-development costly. In this paper we explore our experiences developing a Simulated Context (SimCon) Model which currently supports taking information from a Virtual Reality (VR) SB and converting it into three types of location context to conduct early rapid evaluation of location based SBAs. This model is expressed using the Sensor Modelling Language (sensorML). It also explores the integration of this model into the Industry Foundation Classes (IFC) for modelling and simulating SBs. It also details usability evaluations of the SimConfig and SimConViz Tool for improving evaluation during the design phase of smart building development life cycle.

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DOI: 10.3217/jucs-016-09-1152

Authors: James Decraene, Thomas Hinze

Abstract: All processes of life are controlled by networks of interacting biochemical components. The purpose of modelling these networks is manifold. From a theoretical point of view it allows the exploration of network structures and dynamics, to find emergent properties or to explain the organisation and evolution of networks. From a practical point of view, in silico experiments can be performed that would be very expensive or impossible to achieve in the laboratory, such as hypothesis-testing with regards to knock-out experiments or overexpression, or checking the validity of a proposed molecular mechanism. The literature on modelling biochemical networks is growing rapidly and the motivations behind different modelling techniques are sometimes quite distant from each other. To clarify the current context, we review several of the most popular methods and outline the strengths and weaknesses of deterministic, stochastic, probabilistic, algebraic and agent-based approaches. We then present a comparison table which allows one to identify easily key attributes for each approach such as: the granularity of representation or formulation of temporal and spatial behaviour. We describe how through the use of heterogeneous and bridging tools, it is possible to unify and exploit desirable features found in differing modelling techniques. This paper provides a comprehensive survey of the multidisciplinary area of biochemical networks modelling. By increasing the awareness of multiple complementary modelling approaches, we aim at offering a more comprehensive understanding of biochemical networks.

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DOI: 10.3217/jucs-016-07-1036

Authors: Monika Lanzenberger, Jennifer Sampson, Markus Rester

Abstract: Ontologies are used to represent a variety of domain knowledge and data collections, scopes, viewpoints and linked heterogeneous information sources. They range from simple topologies to highly structured knowledge bases with complex relations. When mapping or aligning two or more ontologies an efficient user support is needed so that the users can understand the prerequisites and the consequences of the alignments. Information Visualization techniques can help to facilitate user understanding of the ontology alignment results. In general, a lot of work in visualization of ontologies exist. We found an enormous number of ontology visualization tools by a literature study. Many of them apply graph visualization but there are other approaches as well. We have identified interesting solutions for dealing with the complexity of large ontologies. Ontology engineering, ontology mapping and alignment can benefit from Information Visualization. Our collection is a starting point to demonstrate the usefulness of Information Visualization techniques, however, a detailed evaluation would be the next step to consolidate this research area and help to boost the adoption of ontologies in common Web applications.

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DOI: 10.3217/jucs-016-01-0082

Authors: Cengiz Kahraman, Selcuk Cebi, İhsan Kaya

Abstract: Renewable energy is a source of energy derived from natural resources such as sunlight, wind, water, tides, hot dry rocks, magma, hot water springs, fire wood, animal manure, and crop residues and waste. These renewable energy technologies are called with its source such as solar power, wind power, hydropower, geothermal and biomass. Energy resources are so important in perspective of economics and politics for all countries. Hence, the selection of the best alternative for any country takes an important role for energy investments. In this paper, axiomatic design (AD) methodology is suggested for the selection among renewable energy alternatives under fuzzy environment. AD methodology works under fuzziness which evaluates the alternatives under objective or subjective criteria with respect to the functional requirements obtained from experts. In the application of the proposed methodologies the most appropriate renewable energy alternative is determined for Turkey under fuzziness.

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DOI: 10.3217/jucs-016-01-0062

Authors: Francisco Cabrerizo, Rubén Heradio, Ignacio Pérez, Enrique Herrera-Viedma

Abstract: In group decision making situations, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved and, as a result, they may present incomplete information. In this paper, we present a new selection process to deal with incomplete fuzzy linguistic information. As part of it, we use an iterative procedure to estimate the missing information. This procedure is guided by the additive consistency property and only uses the preference values provided by the experts. In addition, the additive consistency property is also used to measure the level of consistency of the information provided by the experts. The main novelties of this selection process are both the possibility to manage decision situations under incomplete fuzzy linguistic information and the importance of the experts' preferences in the aggregation processes is modeled by means of the experts' consistency.

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DOI: 10.3217/jucs-016-01-0037

Authors: Mourad Oussalah, Zahir Messaoudi, Abdelaziz Ouldali

Abstract: The purpose of this paper is to address some theoretical issues related to the track-to-track fusion problem when the measurements tracking the same target are inherently correlated by the common process noise of the underlying target. This problem has been intensively investigated using standard Kalman filter with some appealing theoretical results, however such results are no longer valid in case of suboptimality due to either the presence of strong nonlinearity or to the discrete uncertainty pervading the origin of the measurement. This paper reviews several architectures of parallelized blocks of Kalman filters, including the augmented stacked measurement, sequential and data compression architectures. Next, convex combination architecture will be investigated and some theoretical results concerning its extension as well as in case of presence of correlation are investigated. Two special cases of correlation are highlighted. This concerns the case of presence of only two correlated tracks among all tracks and the case of weak correlation. In both cases some original theoretical results are put forward. Finally, links with related fusion architectures is investigated.

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DOI: 10.3217/jucs-015-17-3246

Authors: Yannick Pencolé, Audine Subias

Abstract: This paper addresses the problem of diagnosability analysis in Web Services. In particular, it focuses on the analysis of the impact of time to the diagnostic capabilities in Web Service workflows. The diagnosability analysis that is proposed in this paper aims at determining the diagnostic capabilities of a previously developped algorithm for the diagnosis of Web Services. This diagnostic algorithm is based on chronicle recognitions. Faults that can occur during the execution of service workflows are described by means of chronicles. To perform this diagnosability analysis, the problem is firstly defined as a languagebased analysis which leads to the definition of exclusiveness tests between the languages represented by the chronicles. To deal with the time aspects inherent to the chronicles, we then propose to perform the automatic analysis by the use of time Petri nets. Exclusiveness tests are then defined on reachability graphs of time Petri nets which implicitly represent chronicle languages.

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DOI: 10.3217/jucs-015-17-3231

Authors: Achraf Telmoudi, Lotfi Nabli, Radhi Hiri

Abstract: In this article a new Control-Supervisory architecture of Flexible Manufacturing Systems (FMS) is presented. We are interested particularly in construction and modelling of FMS robust control of flow-shop type to time constraints. Other than the control of production system, the goal is to observe and interpreted the robustness of resources and of manufacturing system. The P-time Petri Nets which is used for modeling of the time constraints. A methodology of construction of a robust control system generating the margins of passive and active robustness is elaborated. The redundancy of the robustness of the elementary parameters between passive and active leads us to define the ways ensuring the total robus tness of the system. To do so, a set of definitions lemmas and theorems are developed and affirmed by examples of applications.

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DOI: 10.3217/jucs-015-13-2676

Authors: Guido Boella, Leendert Van Der Torre, Serena Villata

Abstract: We introduce an approach to iteratively design `small' social networks used in software engineering together with methods analyzing the cooperation in the system. The degree of cooperation is measured by the emergence of coalitions and their stability over time. At the most abstract level, which we call the coalition view, coalitions are abstract entities that may dominate or attack other coalitions. During iterative design, these abstract entities are refined with agents and their dependencies constituting the coalitions (dependence view), the powers of sets of agents to see to goals (power view) and finally the beliefs, plans, tasks and goals of agents (agent view). The analysis methods predict the emergence of coalitions based on reciprocity and argumentation theory.

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DOI: 10.3217/jucs-015-13-2629

Authors: Lăcrămioara Aştefănoaei, Mehdi Dastani, John-Jules Meyer, Frank De Boer

Abstract: This paper presents a programming language that facilitates the implementation of coordination artifacts which in turn can be used to regulate the behaviour of individual agents. The programming language provides constructs inspired by social and organisational concepts. Depending on the scheduling mechanism of such constructs, different operational semantics can be defined. We show how one such possible operational semantics can be prototyped in Maude, which is a rewriting logic software. Prototyping by means of rewriting is important since it allows us both to design and to experiment with the language definitions. To illustrate this, we define particular properties (like enforcement and regimentation) of the coordination artifacts which we then verify with the Maude LTL model-checker.

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DOI: 10.3217/jucs-015-04-0898

Authors: Igor Čavrak, Armin Stranjak, Mario Žagar

Abstract: In this paper we analyze existing methods and languages for modeling agent interactions and propose a SDLMAS Framework for rapid design, development and runtime support of multi-agent systems. The framework provides a simple but expressive declarative language for modeling complex interactions among agents. Proposed language is based on scenarios, sequences of conversation actions directed towards achieving a goal. Scenario descriptions are converted into program code for a chosen target agent platform and system execution is supported by a runtime framework.

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DOI: 10.3217/jucs-014-15-2512

Authors: Ana Olivera, Mariano Frutos, Jessica Carballido, Nélida Brignole

Abstract: This paper describes a new hybrid technique that combines a Greedy Randomized Adaptive Search Procedure (GRASP) and a genetic algorithm with simulation features in order to solve the Bus-Network Scheduling Problem (BNSP). The GRASP is used as an initialization method to find the routes between bus stops. T he Genetic Algorithm is used to find the whole configuration of the bus network, together with a simulation tool that finds the values of the environmentally dependent dynamic variables. The new method was tested with an academic case of study, and the results clearly satisfy the requirements of both the transport user and the transport operator.

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DOI: 10.3217/jucs-014-13-2212

Authors: Miguel Pérez-Toledano, Amparo Navasa, Juan-Manuel Murillo-Rodríguez, Carlos Canal

Abstract: One focus of current software development is the re-use of components in the construction of systems. Software Adaptation facilitates the consequent need to adapt these components to the new environment by employing adaptors which are obtained automatically and hence with a certain guarantee of suitability, from formal descriptions of the interface behaviour. One appropriate technique for Software Adaptation is Aspect-Oriented Programming (AOP) which makes use of aspects to facilitate the dynamic adaptation of components transparently and non-intrusively. However, owing to the way that aspects are integrated, these can unexpectedly modify the functionality of the system, and consequently completely alter its semantics. It is hence necessary to study the final behaviour of the system to ensure its correctness after adding aspects for its adaptation. This study must go beyond just detecting problems at the protocol level, to analyze the potential semantic problems. This is the main focus of the present communication. We start from the Unified Modeling Language (UML 2.0) specification of both the initial system and the aspects. This specification is validated by generating an algebraic Calculus of Communicating Systems (CCS) description of the system. Next, extended (finite) state machines are automatically generated to verify, simulate, and test the modeled system's behaviour. The result of that process can also be compared with the behaviour of the new running system. To facilitate this task, we propose grouping components so as to centre the study on the points actually affected by the behaviour of the aspects.

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DOI: 10.3217/jucs-014-12-2034

Authors: Michael Altenhofen, Andreas Friesen, Jens Lemcke

Abstract: We give a survey on work we did in the past where we have successfully applied the ASM methodology to provide abstract models for a number of problem areas that are commonly found in Service Oriented Architectures (SOA). In particular, we summarize our work on (1) service behavior mediation, (2) service discovery, and (3) service composition, showing that the corresponding solutions can be described as variations of a fundamental abstract processing model—the Virtual Provider.

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DOI: 10.3217/jucs-014-12-2007

Authors: Martin Ouimet, Kristina Lundqvist

Abstract: In this paper, we present the Timed Abstract State Machine (TASM) language, which is a language for the specification of embedded real-time systems. In the engineering of embedded real-time systems, the correctness of the system is defined in terms of three aspects - function, time, and resource consumption. The goal of the TASM language and its associated toolset is to provide a basis for specification-based real-time system engineering where these three aspects can be specified and analyzed. The TASM language is built on top of Abstract State Machines (ASM) by including facilities for compact and legible specification of non-functional behavior, namely time and resource consumption. The TASM language provides a notation which is well-suited to the specification needs of embedded real-time systems. We begin the presentation of the language with a historical survey on the use of ASM in specifying real-time systems. The core difference between the TASM language and ASM is that steps are inherently durative instead of being instantaneous and steps consume resources. These concepts capture the reality of physical systems in a flexible abstract model. We present the syntax and semantics of the language and illustrate the concepts using an extended version of the production cell case study.

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DOI: 10.3217/jucs-014-10-1763

Authors: Abdelhak Aqqal, Christoph Rensing, Ralf Steinmetz, Najib Elkamoun, Abdelghafour Berraissoul

Abstract: Recently Web Based Training (WBT) starts to be widely used as a new way of teaching. Unfortunately, this mode of teaching imposes new requirements and constraints. It has made the creation of learning material a complex and demanding task for the instructors as it takes much time and demands a multitude of skills, in particular technical skills that must be developed and continuously updated. Hence, we propose a collaborative authoring methodology based on division of labour as a way to produce WBTs where the processes of production are clearly separated to meet the existing and needed skills of persons involved in WBT production. This paper presents an efficient method to support instructor’s guidance during the first phase of the WBT production called the Macro Design using the Rhetorical Structure Theory (RST) and the taxonomies we developed.

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DOI: 10.3217/jucs-014-09-1480

Authors: Jose Panach, Sergio España, Inés Pederiva, Óscar Pastor

Abstract: Currently, many models are used to capture functional software requirements. However, the Software Engineering community has faded interaction requirements into the background, dealing with interface mainly in design time. A sound MDA-compliant software development methodology, called OO-Method, is extended in this work to bridge this gap. The issue is to define a methodology for capturing interaction requirements. For this purpose, the formal notation ConcurTaskTrees (CTT) is used. This notation is a technique that is well-known in the Human Computer Interaction community. A set of interaction patterns has been defined to build CTT models. These patterns are defined with a very precise syntax and semantics. Moreover, transformation rules are defined to transform the Task Model into the OO-Method Presentation Model, which specifies the user interface in an abstract and platform-independent way. However, since editing the CTT models is hard work, this paper proposes superimposing a layer to the CTT diagram in order to capture interaction requirements using sketches. CTT models will be synchronously generated from these sketches. Because this transformation is 'transparent' to the analyst, he only needs to draw the sketches during the interaction requirements elicitation. The approach presented in this paper is instantiated for the environment of the OLIVANOVA technology. This environment makes it possible to obtain a final software product from its corresponding Conceptual Model through a Model Compilation process, where interaction modeling is properly embedded with the most conventional data and process modeling.

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DOI: 10.3217/jucs-014-07-1136

Authors: Koichi Moriyama, Mitsuhiro Matsumoto, Ken-ichi Fukui, Satoshi Kurihara, Masayuki Numao

Abstract: In recent years, market forecasting by machine learning methods has been flourishing.Most existing works use a past market data set, because they assume that each trader's individual decisions do not affect market prices at all. Meanwhile, there have been attempts to analyzeeconomic phenomena by constructing virtual market simulators, in which human and artificial traders really make trades. Since prices in a market are, in fact, determined by every trader'sdecisions, a virtual market is more realistic, and the above assumption does not apply. In this work, we design several reinforcement learners on the futures market simulator U-Mart (UnrealMarket as an Artificial Research Testbed) and compare our learners with the previous champions of U-Mart competitions empirically.

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DOI: 10.3217/jucs-014-07-1080

Authors: Vedran Podobnik, Ana Petric, Gordan Jezic

Abstract: Supply chain management (SCM) deals with planning and coordinating activities such as material procurement, product assembly, and the distribution of manufactured products. This paper offers an agent-based solution as a potentially adequate approach for the automation of supply chain management. The greatest obstacle in SCM research is obtaining benchmark designed solutions since it is difficult to simulate real business environments, while live testing in real-world systems is not an option. The Trading Agent Competition Supply Chain Management (TAC SCM) scenario provides a unique testbed for studying and prototyping SCM agents by providing a challenging game environment where competing agents engage in complex decision-making activities with the purpose of maximizing their profit. In this paper, we describe the TAC SCM environment and present the main features of the CrocodileAgent, our TAC SCM 2007 entry. Additionally, the CrocodileAgent’s performance in the competition, as well as in a series of controlled experiments, is discussed.

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DOI: 10.3217/jucs-014-07-1061

Authors: Panagiotis Papazoglou, Dimitrios Karras, Rallis Papademetriou

Abstract: Multi-Agent Systems (MAS) constitute a well known approach in modelling dynamical real world systems. Recently, this technology has been applied to Wireless Communication Systems (WCS), where efficient resource allocation is a primary goal, for modelling the physical entities involved, like Base Stations (BS), service providers and network operators. This paper presents a novel approach in applying MAS methodology to WCS resource allocation by modelling more abstract entities involved in WCS operation, and especially the concurrent network procedures (services). Due to the concurrent nature of a WCS, MAS technology presents a suitable modelling solution. Services such as new call admission, handoff, user movement and call termination are independent to one another and may occur at the same time for many different users in the network. Thus, the required network procedures for supporting the above services act autonomously, interact with the network environment (gather information such as interference conditions), take decisions (e.g. call establishment), etc, and can be modelled as agents. Based on this novel simulation approach, the agent cooperation in terms of negotiation and agreement becomes a critical issue. To this end, two negotiation strategies are presented and evaluated in this research effort and among them the distributed negotiation and communication scheme between network agents is presented to be highly efficient in terms of network performance. The multi-agent concept adapted to the concurrent nature of large scale WCS is, also, discussed in this paper.

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DOI: 10.3217/jucs-014-01-0004

Authors: Werner Geyer, Roberto S. Silva Filho, Beth Brownholtz, David Redmiles

Abstract: Contextual collaboration seamlessly integrates existing groupware technologies into a uniform user experience that combines synchronous and asynchronous interactions. This user experience is usually supported by a collaboration infrastructure that needs to efficiently cope with the fast switching and integration of different modes of interaction. In this paper, we study a model for contextual collaboration that supports multiple modalities of collaboration. Our model is based on generic shared objects that provide building blocks for supporting contextual collaboration applications. We describe a native implementation of this model and evaluate its behavior under different media traffic conditions. We compare the native implementation with an alternative implementation that integrates existing notification and meeting servers to deliver the same model behavior. We discuss trade-offs and limitations of those two implementations.

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DOI: 10.3217/jucs-013-10-1484

Authors: Tao Zeng, Changjie Tang, Yong Xiang, Peng Chen, Yintian Liu

Abstract: Rule mining is an important issue in data mining. To address it, a novel Immune Gene Expression Programming (IGEP) model was proposed. Concepts of rule, gene, immune cell, and antibody were formalized. The dynamic evolution models and the corresponding recursive equations of immune cell, self, immune-tolerance were built. The novel key techniques of IGEP were presented. Experiment results showed that the new method has good stability, scalability and flexibility. It can discover traditional association rule, non-traditional rule including connective "OR" or "NOT", and meta-rule of strong rule. Furthermore, it can perform well in constrained pattern mining.

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DOI: 10.3217/jucs-013-04-0572

Authors: Milena Vujošević-Janičić, Jelena Tomašević, Predrag Janičić

Abstract: We present a new type of sat problem called the k-GD-SAT, which generalizes k-sat and GD-sat. In k-GD-SAT, clause lengths have geometric distribution, controlled by a probability parameter p; for p = 1, a k-GD-SAT problem is a k-SAT problem. We report on the phase transition between satisfiability and unsatisfiability for randomly generated instances of k-GD-SAT. We provide theoretical analysis and experimental results suggesting that there is an intriguing relationship (linear in the parameter 1/p) between crossover points for different parameters of k-GD-SAT. We also consider a relationship between crossover points for k-SAT and k-GD-SAT and provide links between these values.

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DOI: 10.3217/jucs-012-09-1312

Authors: Benedek Nagy, László Szegedi

Abstract: In this paper a comparison is provided between the membrane computing systems and the graphical interfaces of operating systems. A membrane computing system is a computing model using massive parallelism inspired by the functioning of living cells. The graphical schemes of these computing devices look like the windows of a graphical operating system representing programs running parallel on the computer. Both similarities and differences of membrane-systems and graphical operating systems are detailed as well as some possible simulation methods.

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DOI: 10.3217/jucs-012-06-0608

Authors: Kazuyuki Nakamura, Tomoyuki Higuchi, Naoki Hirose

Abstract: Data assimilation is a method of combining an imperfect simulation model and a number of incomplete observation data. Sequential data assimilation is a data assimilation in which simulation variables are corrected at every time step of observation. The ensemble Kalman filter is developed for a sequential data assimilation and frequently used in geophysics. On the other hand, the particle filter developed and used in statistics is similar in view of ensemble-based method, but it has different properties. In this paper, these two ensemble based filters are compared and characterized through matrix representation. An application of sequential data assimilation to tsunami simulation model with a numerical experiment is also shown. The particle filter is employed for this application. An erroneous bottom topography is corrected in the numerical experiment, which demonstrates that the particle filter is useful tool as the sequential data assimilation method.

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DOI: 10.3217/jucs-012-04-0370

Authors: Giuseppe Ascia, Vincenzo Catania, Maurizio Palesi

Abstract: Advances in technology now make it possible to integrate hundreds of cores (e.g. general or special purpose processors, embedded memories, application specific components, mixed-signal I/O cores) in a single silicon die. The large number of resources that have to communicate makes the use of interconnection systems based on shared buses inefficient. One way to solve the problem of on-chip communications is to use a Network-on-Chip (NoC)-based communication infrastructure. Such interconnection systems offer new degrees of freedom, exploration of which may reveal significant optimization possibilities: the possibility of arranging the computing and storage resources in an NoC, for example, has a great impact on various performance indexes. The paper addresses the problem of topological mapping of intellectual properties (IPs) on the tiles of a mesh-based NoC architecture. The aim is to obtain the Pareto mappings that maximize performance and minimize power dissipation. We propose a heuristic technique based on evolutionary computing to obtain an optimal approximation of the Pareto-optimal front in an efficient and accurate way. At the same time, two of the most widely-known approaches to mapping in mesh­based NoC architectures are extended in order to explore the mapping space in a multi-criteria mode. The approaches are then evaluated and compared, in terms of both accuracy and efficiency, on a platform based on an event-driven trace-based simulator which makes it possible to take account of important dynamic effects that have a great impact on mapping. The evaluation performed on both synthesized traffic and real applications (an MPEG-4 codec) confirms the efficiency, accuracy and scalability of the proposed approach.

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DOI: 10.3217/jucs-011-11-1849

Authors: Denis Popel

Abstract: The data mining community is focused on a variety of methods and algorithms to manipulate incompletely specified or uncertain data and their dependencies. The major obstacle in the representation and visualization of incompletely specified data is the size explosion problem through defining undefined or uncertain values, which commonly raises questions about suggested heuristics and their practical applicability. Recently, there is a renewed interest in resolving the size explosion problem for incompletely specified and uncertain data based on symbolic techniques. One of such techniques, decision diagram, has been successfully applied to many knowledge visualization and data manipulation problems.

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DOI: 10.3217/jucs-011-06-0804

Authors: Alireza Bagheri, Mohammadreza Razzazi

Abstract: In this paper we investigate polyline grid drawing of free trees on 2D grids which are bounded by simple polygons. We focus on achieving uniform node distribution while we also try to achieve minimum edge crossings. We do not consider achieving symmetry as a mandatory task, but our algorithm can exploit some symmetries present in both the given trees and the given polygons. To our knowledge, our work is the first attempt for developing algorithms that draw graphs on regions which are bounded by simple polygons.

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DOI: 10.3217/jucs-011-04-0452

Authors: Norbert Gronau, Claudia Müller, Roman Korf

Abstract: Existing approaches in the area of knowledge-intensive processes focus on integrated knowledge and process management systems, the support of processes with KM systems, or the analysis of knowledge-intensive activities. For capturing knowledge-intensive business processes well known and established methods do not meet the requirements of a comprehensive and integrated approach of process-oriented knowledge management. These approaches are not able to visualise the decisions, actions and measures which are causing the sequence of the processes in an adequate manner. Parallel to conventional processes knowledge-intensive processes exist. These processes are based on conversions of knowledge within these processes. To fill these gaps in modelling knowledge-intensive business processes the Knowledge Modelling and Description Language (KMDL) got developed. The KMDL is able to represent the development, use, offer and demand of knowledge along business processes. Further it is possible to show the existing knowledge conversions which take place additionally to the normal business processes. The KMDL can be used to formalise knowledge-intensive processes with a focus on certain knowledge-specific characteristics and to identify process improvements in these processes. The KMDL modelling tool K-Modeler is introduced for a computer-aided modelling and analysing. The technical framework and the most important functionalities to support the analysis of the captured processes are introduced in the following contribution.

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DOI: 10.3217/jucs-011-02-0303

Authors: Frantisek Capkovic

Abstract: An application of the method suitable for modelling and control of general discrete event dynamic systems (DEDS) to special kinds of communication systems is presented in this paper. The approach is based on Petri nets (PN) defined in [Peterson 1981] and directed graphs (DG) described e.g. in [Diestel 1997]. It is supported by the previous author s works, especially [Capkovic 2003], [Capkovic 1998], [Tzafestas and Capkovic 1997].

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DOI: 10.3217/jucs-011-02-0271

Authors: Juliusz Kulikowski

Abstract: There is considered a problem of information profiles and information resources collection forming in distributed data- and/or knowledge bases as a result of an attempt to satisfy the information requirements of the customers represented by their information profiles. It is shown that the interests of managers of data- and knowledge bases are not fully convergent and that they participate in a composite, partially co-operative, partially non-co-operative n-persons games. There is given a formal description of the strategies used in such games, as well as the methods of decision making of the players on the level of open access (OADB) as well as on local (LDB) databases one.

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DOI: 10.3217/jucs-011-01-0037

Authors: In Khor, Johnson Thomas, Istvan Jonyer

Abstract: Existing ad hoc routing protocols are either unicast or multicast. In this paper we propose a simple extension to the Dynamic Source Routing Protocol (DSR) to cater for group communications where all node addresses are unicast addresses and there is no single multicast address. The proposed sliding window protocol for multiple communications results in significant improvement in total packet delivery. Due to the high frequency of mobility, attrition and reinforcement in ad hoc networks, in order to preserve confidentiality, it becomes necessary to rekey each time a member enters or leaves a logically defined group. We compare our group rekeying rate on sliding window protocol versus other kinds of Rekeying algorithms. The proposed sliding window protocol performs better. The proposed sliding window is therefore simple and improves both communications and security performance.

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DOI: 10.3217/jucs-010-12-1693

Authors: Gianpiero Cabodi, Sergio Nocco, Stefano Quer

Abstract: Binary Decision Diagrams (BDDs) have been widely used in synthesis and verification. Boolean Satisfiability (SAT) Solvers, on the other hand, have been gaining ground only recently, with the introduction of efficient implementation procedures. Specifically, while BDDs have been mainly adopted to formally verify the correctness of hardware devices, SAT-based Bounded Model Checking (BMC) has been widely used for debugging. In this paper, we combine BDD and SAT-based methods to increase the efficiency of BMC. We first exploit affordable BDD-based symbolic approximate reachability analysis to gather information on the state space. Then, we use the collected overestimated reachable state sets to restrict the search space of a SAT-based BMC. This is possible by feeding the SAT solver with a description that is the combination of the original BMC problem with the extra information coming from BDD-based symbolic analysis. We develop specific strategies to appropriately mix BDD and SAT efforts, and to efficiently convert BDD-based symbolic state set representations into SAT-oriented ones.Experimental results prove the validity of our strategy to reduce the amount of variable assignments and variable conflicts generated by SAT solvers, with a subsequent significant performance gain. We gather results with four among the most used SAT solvers, namely Chaff, Limmat, BerkMin, and Siege. We could reduce the number of conflicts up to more than 100x, and the verification time up to 30x.

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DOI: 10.3217/jucs-010-10-1469

Authors: Petr Matoušek

Abstract: Protocol analysis involves several parameters in model specification, for instance, transmission delay or the length of the transmitting window. Verification of the model with parameters is a semi-decision process that depends on the number of clocks, parameters and counters in the model. Using combination of different verification tools for timed models as HyTech, TReX and UPPaal we are able to find relation between parameters satisfying desired property. The paper gives a report on the synthesis of parameters of PGM protocol. We built a formal model based on extended time automata with parameters and verified the reliability property. Our results automatically obtained from the model are consistent with previous results derived manually. The paper describes our experience with parametric verification of multicast protocol PGM. Results mentioned in the work were made with collaboration with Mihaela Sighireanu (Mihaela.Sighireanu@liafa.jussieu.fr) from LIAFA, Paris.

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DOI: 10.3217/jucs-010-01-0014

Authors: Maja Pivec, Olga Dziabenko

Abstract: How to design effective learning opportunities? Why is learning by experience often more efficient than learning by studying? How to provide the learning experiences needed to respond to current challenges? Using computer games and games in general for educational purposes offers a variety of knowledge presentations and creates opportunities to apply the knowledge within a virtual world, thus supporting and facilitating learning processes. An innovative educational paradigm such as game-based learning, which is considered suitable for the given purpose, is described in this article. The connection of the collaborative social context of education with game-based learning is discussed in the first part of the paper. The second part of the paper introduces the game concept of "UniGame: Social Skills and Knowledge Training". Game ideas along the educational background of the UniGame game concept are outlined. UniGame scenarios presented and possible use cases should stimulate users to apply game-based learning approach in the future for their classes.

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DOI: 10.3217/jucs-009-11-1277

Authors: Frantisek Scuglik, Miroslav Sveda

Abstract: Two possibilities of automated CSP (Communicating Sequential Processes) support are introduced in [11] and [10] using either behavioral diagrams or application source code. While in the first approach a tool generates CSP specification from behavioral diagrams, based on UML Composite States diagram, in the second approach an application source code is translated directly into CSP specification using a compiler. This paper reviews tools related to both techniques.

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DOI: 10.3217/jucs-009-08-0795

Authors: Nélio Muniz Mendes Alves, Sérgio Schneider

Abstract: This paper describes an ongoing implementation of an embedded hardware description language (HDL) using Haskell as a host language. Traditionally, functional HDL s are made using lazy lists to model signals, so circuits are functions from lists of input values to lists of output values. We use another known approach for embedded languages, in which circuits are data structures rather than functions. This style of implementation permits one to inspect the structure of the circuit, allowing one to perform different interpretations for the same description. The approach we present can also be applied to other domain-specific embedded languages. We provide an elegant implementation of memories and a set of new signal types.

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DOI: 10.3217/jucs-009-03-0223

Authors: Lenore Zuck, Amir Pnueli, Yi Fang, Benjamin Goldberg

Abstract: There is a growing awareness, both in industry and academia, of the crucial role of formally verifying the translation from high-level source-code into low-level object code that is typically performed by an optimizing compiler. Formally verifying an optimizing compiler, as one would verify any other large program, is not feasible due to its size, ongoing evolution and modification, and, possibly, proprietary considerations. Translation validation is a novel approach that offers an alternative to the verification of translators in general and compilers in particular: Rather than verifying the compiler itself, one constructs a validation tool which, after every run of the compiler, formally confirms that the target code produced in the run is a correct translation of the source program. The paper presents voc, a methodology for the translation validation of optimizing compilers. We distinguish between structure preserving optimizations, for which we establish a simulation relation between the source and target code based on computational induction, and structure modifying optimizations, for which we develop specialized permutation rules. The paper also describes voc-64 - a prototype translation validator tool that automatically produces verification conditions for the global optimizations of the SGI Pro-64 compiler.

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DOI: 10.3217/jucs-009-02-0120

Authors: Andreas Krebs, Jürgen Ruf

Abstract: Verification and validation are the major tasks during the design of digital hardware/software systems. Often more than 70% of the development time is spent for locating and correcting errors in the design. Therefore, many techniques have been developed to support the debugging process. Recently, simulation and test methods have been accompanied by formal methods such as equivalence checking and property checking. However, their industrial applicability is currently restricted to small or medium sized designs or to a specific phase in the design process. Therefore, simulation is still the most commonly applied verification technique. In this paper, we present a method for asserting temporal properties during simulation and also during emulation of hardware prototypes. The properties under verification are efficiently translated into an intermediate language (of a virtual machine). This intermediate representation can then be interpreted during simulation. We may also produce executable checkers running in parallel to the simulation. Furthermore, we are able to translate the properties into synthesizable hardware modules which can then be used during system emulation on FPGA-based emulators or as self test components checking the functionality during the lifetime of the system.

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DOI: 10.3217/jucs-009-02-0106

Authors: Hans-Dieter Ehrich, Maik Kollmann, Ralf Pinger

Abstract: We present a method for checking global conditions for object systems in a way that avoids state space explosion. The objects referred to in a global condition are checked step by step against local conditions and communication requirements derived from the global condition. The derivation is automatic, based on information about the system structure contained in the global condition. The approach is demonstrated using model checking, but the idea works for other approaches to verification or testing as well. In our current investigation, a multi-object variant of CTL is used for expressing global conditions. The local conditions and communication requirements can be verified independently using standard model checkers. The method is illustrated by a large example (about 10 24 states) where our method shows a spectacular speedup over global model checking.

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DOI: 10.3217/jucs-008-01-0002

Authors: Egon Börger

Abstract: The research belonging to the Abstract State Machines approach to system design and analysis is surveyed and documented in an annotated ASM bibliography. The survey covers the period from 1984, when the idea for the concept of ASMs (under the name dynamic or evolving algebras or structures) appears for the first time in a foundational context, to the year 2001 where a mathematically well-founded, practical system development method based upon the notion of ASMs is in place and ready to be industrially deployed. Some lessons for the future of ASMs are drawn.

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